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Intelligent and Automatic In vivo Detection and Quantification of Transplanted Cells in MRI

1.
Challenges
Intelligent and Automatic In vivo Detection and Quantification of
Transplanted Cells in MRI
Muhammad Jamal Afridi, Arun Ross, and Erik M. Shapiro
Michigan State University, East Lansing, MI, USA
Objective
Extracting Classification Units
Feature Representation
Labeling ModuleMotivation
Cell transplant therapy is emerging as a promising
solution for treating a myriad of diseases.
However, its success in humans is not fully proven.
Quantifying cell number delivered to a specific organ is
crucial for monitoring success.
Transplanted cells appear as dark spots in MRI scans.
Manual spot enumeration is tedious and time
consuming and cannot be adopted for large scale analysis
Transfer Learning for Small Data
Classification unit for a spot: One pixel, two pixels, how many shall make a unit?
Feature representation: What is the best feature representation for a spot?
Learning with small training data: State-of-the-art machine learning approaches
need large training data. How to train these effectively using only small amount of data?
(1) Expert designed spot features (P-1)
(2) Deep learning based automatically designed features (P-2)
Contributions
First comprehensive machine learning based research
on automated cell detection in MRI.
First, labelled cellular MRI database collection with
more than 20,000 manual labels.
Experimental results show a detection accuracy of up
to 97.3% in vivo and 99.8%.
Using transfer learning, with only 5% data, up to 88%
in vivo accuracy can be achieved.
Collected database
Extracted patches
Top PCA shapes Binary shape filters Context features
Results and Comparison
To automatically detect these spots in vivo.
These feature representations are manually designed by an expert
These feature representations are automatically learned from data
in vitro in vivo
LR
Comparing spot numbers:
Each tube is
expected to have
a spot number
of ~2400
(B) shows a labeled MRI sliceDesigned Labeling tool
in vitro scan in vivo scan
{afridimu, rossarun, erik.shapiro}@msu.edu
Spot Appearance
Labeled
MSCs
Unlabeled
MSCs
Analyzing robustness:
Future
Work
3D Visualization
We are thankful to the support provided by NIH grants
DP2OD004362 (EMS), R21 CA185163(EMS),
R01 DK107697(EMS), R21 AG041266 (EMS).